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Title: Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues
Authors: Schwartz, Jean-Marc
Otokuni, Hiroaki
Akutsu, Tatsuya  kyouindb  KAKEN_id  orcid (unconfirmed)
Nacher, Jose C.
Author's alias: 阿久津, 達也
Keywords: Biochemical reaction networks
Cancer metabolism
Probabilistic data networks
Issue Date: 20-Jun-2019
Publisher: Springer Science and Business Media LLC
Journal title: Nature Communications
Volume: 10
Thesis number: 2725
Abstract: Recent research has shown that many types of cancers take control of specific metabolic processes. We compiled metabolic networks corresponding to four healthy and cancer tissues, and analysed the healthy–cancer transition from the metabolic flux change perspective. We used a Probabilistic Minimum Dominating Set (PMDS) model, which identifies a minimum set of nodes that act as driver nodes and control the entire network. The combination of control theory with flux correlation analysis shows that flux correlations substantially increase in cancer states of breast, kidney and urothelial tissues, but not in lung. No change in the network topology between healthy and cancer networks was observed, but PMDS analysis shows that cancer states require fewer controllers than their corresponding healthy states. These results indicate that cancer metabolism is characterised by more streamlined flux distributions, which may be focused towards a reduced set of objectives and controlled by fewer regulatory elements.
Rights: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit
DOI(Published Version): 10.1038/s41467-019-10616-z
PubMed ID: 31221963
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